Most deep da techniques applied to the object detection task rely on two-stage detectors (see also sec:back:cnn-based-detectors for further details) because they produce, as the first step, a set of regions that have therefore been classified in the second stage. Considering this region selection mechanism being domain-independent, deep da strategies can be helpful in the second-stage classification process to adapt to the target domain. In other words, deep da methods act in the second stage of the detection pipeline, where R-CNNs train classifiers over the proposed regions. To this end, most works exploit the limited images annotated with bounding boxes to learn to detect and, in parallel, use weakly labeled data (such as image-level class labels) during the second stage to adapt the detector to the target domain. For instance, Hoffman et al. [1]} proposed the Large-Scale Detection through Adaptation (LSDA) network where, firstly, a classification layer is trained for the target domain; then, a pre-trained source detection model is employed, together with output layer adaptation strategies aiming at update the target classification parameters. Instead, Chen et al. [2]} considered the Faster R-CNN detector incorporating an image-level and an instance-level adaptation component in the detection pipeline; they minimized the domain discrepancy by exploiting an adversarial strategy during the training phase.
